Canadian Wind Turbines

Point Symbol Map

Photo by Keshav Rajasekar on Unsplash

The answer my friend is blowin’ in the wind…
— Bob Dylan

Ingest

# Data source must have an ISO3 field
df <- read_csv("archetypes/canadian-windmills/wind_turbine.csv")
df

Wrangle

df_wrangled <- df %>%
  # group the data by province/territory to calculate summary statistics
  group_by(province_territory) %>%
  # now summarize the data for count and mean of variables
  summarise(count=n(), # count the number of windmills in each province
            meandiam=mean(rotor_diameter_m), # mean of their diameters
            meanlat=mean(latitude), # mean of their latitudes
            meanlong=mean(longitude))

df_wrangled

Map of Canada

# Filter and keep only map of Canada
# using ggplot2 built-in maps
canada_map <- map_data("world") %>% 
  filter(region=="Canada")


map <- ggplot() +
       geom_polygon(data=canada_map,
               aes(x=long, y=lat, group=group), fill="grey90", color="white") +
       theme_void()

girafe(ggobj = map, width_svg = 16, height_svg = 12,
       options = list(opts_sizing(rescale = TRUE, width = 0.5))
)

Plot

fill by “water source” status

v1 <- ggplot(df_wrangled) +
  # add a map of Canada
  geom_polygon(data=canada_map,
               aes(x=long, y=lat, group=group), fill="grey90", color="white") +
  # add a point and star at each mean windmill location
  geom_point(aes(x=meanlong, y=meanlat, color=count), size=4, shape=20) +
  geom_point(aes(x=meanlong, y=meanlat, size=meandiam, color=count), shape=8) +
  # add labels for the points
  geom_text_repel(aes(x=meanlong, y=meanlat, color=count,
                      label=str_wrap(province_territory, 14)), # wrap the labels so they fit better
                  nudge_y=1.5, family="Amaranth", segment.alpha=0) +
  # change the color scheme
  scale_color_gradient(low="red3",high="black") +
  # add overall theme and fix the coordinates so the map doesn't warp
  theme_void() + 
  coord_fixed(1.3) +
  # edit the theme and legend position
  theme(text=element_text(family="Amaranth"),
        legend.position=c(0.9,0.7), 
        legend.box="horizontal", 
        legend.direction = "vertical") +
  # add title to the top left of the plot
  geom_text(aes(x=-110, y=78, label="Canadian Windmills by Province/Territory"),
            size=7, fontface="bold", family="Amaranth") +
  # add subtitle under the title
  geom_text(aes(x=-110, y=76, 
                label="Each point represents the mean location, size, and diameter of the windmills in the territory."),
            size=4, family="Amaranth", color="red4") +
  # add caption on bottom left inset
  geom_text(aes(x=-125, y=43, 
                label="data from open.canada.ca | plot by @rjstevick for #TidyTuesday"),
            size=3, family="Amaranth", color="grey60") +
  # add those labels
  labs(color="Number of \nWindmills", 
       size="Mean diameter \nof Windmills (m)")


girafe(ggobj = v1, width_svg = 16, height_svg = 12,
       options = list(opts_sizing(rescale = TRUE, width = 1.0))
)

References

citations for narrative and data sources

Narrative sources: This is a reproduction of an original plot by Rebecca Stevick for #TidyTuesday, GO
Data sources: TidyTuesday 2020 week 44 - Canadian Wind Turbines, GO